Feedback of real-time fMRI signals: From concepts and principles to therapeutic interventions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe feedback of real-time functional magnetic resonance imaging (rtfMRI) signals, dubbed “neurofeedback”, has found applications in the treatment of clinical disorders and enhancement of brain performance. However, knowledge of the basic underlying mechanism on which neurofeedback is based is rather limited. This article introduces the concepts, principles and characteristics of feedback control systems and its applications to electroencephalography (EEG) and rtfMRI signals. Insight into the underlying mechanisms of feedback systems may lead to the development of novel feedback protocols and subsystems for rtfMRI and enhance therapeutic solutions for clinical interventions

Children with ADHD exhibit lower fMRI spectral exponent than their typically developing counterparts

The file attached to this record is the author's final peer reviewed version.Complex interactions in nonlinear systems such as the human brain exhibit fractal processes which are outcomes of self-similar patterns over long time scales by a power law in the frequency domain. The spectral exponent (γ) of this power law can be observed as an estimator of relative health and disease especially in the case of 1/f power spectrum. The aim of this pilot study is to estimate the fractal behaviour (using spectral exponent) of resting state fMRI time series of children with ADHD when compared to age-matched and gender-matched typically developing children (TDC). We expect the spectral exponent of the children with ADHD to be significantly different from that of their typically developing counterparts. Our analysis shows that both the children with ADHD and TDC exhibited positive spectral exponent (γ) which implies that their fMRI time series depicts greater power at high frequencies. However, the children with ADHD exhibited significantly (p<0.05) lower spectral exponent (γ) than their typically developing counterparts in brain regions consistent with abnormalities in ADHD brain dynamics. Our results have shown that spectral exponent (γ) may be a useful tool in revealing abnormalities in ADHD brain dynamics

Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkSome studies have placed Sample entropy on the same data length constraint of 10m–20m (m: pattern length) as approximate entropy, even though Sample entropy is largely independent of data length and displays relative consistency over a broader range of possible parameters (r, tolerance value; m, pattern length; N, data length) under circumstances where approximate entropy does not. This is particularly erroneous for some fMRI experiments where the working data length is less than 100 volumes (when m = 2). We therefore investigated whether Sample entropy is able to effectively discriminate fMRI data with data length, N less than 10m (where m = 2) and r = 0.30, from a small group of 10 younger and 10 elderly adults, and the whole cohort of 43 younger and 43 elderly adults, that are significantly (p 0.05)

Using real-time fMRI brain-computer interfacing to treat eating disorders

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkReal-time functional magnetic resonance imaging based brain-computer interfacing (fMRI neurofeedback) has shown encouraging outcomes in the treatment of psychiatric and behavioural disorders. However, its use in the treatment of eating disorders is very limited. Here, we give a brief overview of how to design and implement fMRI neurofeedback intervention for the treatment of eating disorders, considering the basic and essential components. We also attempt to develop potential adaptations of fMRI neurofeedback intervention for the treatment of anorexia nervosa, bulimia nervosa and binge eating disorder

Development of a Technique for Restoring the Fidelity of Distorted Playback Audio Signal from Analog Cassette Tape

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkA simple yet elegant analog based technique for restoring the fidelity of playback audio signals emanating from magnetic cassette tapes is presented. The technique makes use of information from the high frequency bias signal in magnetic cassette tapes to correct for errors in the playback audio signal. Performance evaluation of the developed technique shows that the technique can correct for errors due to noise, scratches on the tape surface, clipping, and non-linear distortion. The developed technique will be valuable in restoring the fidelity of playback audio signal from magnetic cassette tapes stored in archives and private homes

Fractal analysis of resting state fMRI signals in adults with ADHD

The fractal concept developed by Mandelbrot provides a useful tool for examining a variety of naturally occurring phenomena. Fractals are signals that display scale-invariant or self-similar behaviour. They can be found everywhere in nature including fractional Gaussian noise (fGn). Resting state fMRI signals can be modelled as fGn which makes them appropriate for fractal analysis. The Hurst exponent, H, is a measure of fractal processes and has values ranging between 0 and 1. Fractional Gaussian noise with 0<H<0.5 demonstrates negatively autocorrelated or antipersistent behaviour; fGn with 0.5<H<1 demonstrates a positively correlated, relatively persistent, predictable, long memory behaviour; and fGn with H = 0.5 corresponds to classical Gaussian white noise. In the present study, we aim to estimate the fractal behaviour of adult ADHD patients when compared to age-matched healthy controls using dispersional analysis. We hypothesize that ADHD patients will demonstrate more predictable (higher H values) fractal behaviour. Ten ADHD patients (5 female, mean age (32.60±10.46)) and ten controls (7 female, mean age (30.10±8.49)) were brain imaged by 3T MRI scanner. All patients and control participants completed the Conners’ Adult ADHD Rating Scales (ADHD scores). Our analysis shows that the ADHD patients demonstrate more positively correlated, relatively persistent, predictable and longer memory fractal behaviour in regards to healthy controls. The discriminated brain regions are part of the frontal-striatal-cerebellar circuits and are consistent with the hypothesis of abnormal frontal-striatal-cerebellar circuits in ADHD. We have shown that the analysis of fractal behaviour may be a useful tool in revealing abnormalities in ADHD brain dynamics

Resting state fMRI entropy probes complexity of brain activity in adults with ADHD

In patients with attention deficit hyperactivity disorder (ADHD), quantitative neuroimaging techniques have revealed abnormalities in various brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Nonlinear signal processing techniques such as sample entropy have been used to probe the regularity of brain magnetoencephalography signals in patients with ADHD. In the present study, we extend this technique to analyse the complex output patterns of the 4 dimensional resting state functional magnetic resonance imaging signals in adult patients with ADHD. After adjusting for the effect of age, we found whole brain entropy differences (P=0.002) between groups and negative correlation (r=-0.45) between symptom scores and mean whole brain entropy values, indicating lower complexity in patients. In the regional analysis, patients showed reduced entropy in frontal and occipital regions bilaterally and a significant negative correlation between the symptom scores and the entropy maps at a family-wise error corrected cluster level of P<0.05 (P=0.001, initial threshold). Our findings support the hypothesis of abnormal frontal-striatal-cerebellar circuits in ADHD and the suggestion that sample entropy is a useful tool in revealing abnormalities in the brain dynamics of patients with psychiatric disorders

Functional MRI entropy measurements of age-related brain changes

As we age there is a decline in cognitive abilities such as processing speed, memory, executive function and reasoning. The basis for this decline is not well understood. In this study, the physiological complexity of resting state fMRI signals in a group of healthy volunteers was investigated. Twenty volunteers ranging from age 25 to 60 years underwent functional magnetic resonance imaging (fMRI). Physiological complexity was measured by calculating approximate entropy (ApEn) maps for all volunteers. Maps were statistically analysed globally and regionally with Statistical Package for Social Sciences (SPSS) and Statistical Parametric Mapping (SPM8) software respectively. Comparing the older participants (> 40 years) with the younger ones, the older group exhibited significantly lower signal ApEn in areas of white matter, grey matter, frontal lobe, sub-lobar, brainstem, limbic lobe and temporal lobe. Decline in fMRI brain complexity is a feature of normal ageing beyond the age of 40 years

Targeting the affective brain-a randomized controlled trial of real-time fMRI neurofeedback in patients with depression.

open access articleFunctional magnetic resonance imaging neurofeedback (fMRI-NF) training of areas involved in emotion processing can reduce depressive symptoms by over 40% on the Hamilton Depression Rating Scale (HDRS). However, it remains unclear if this efficacy is specific to feedback from emotion-regulating regions. We tested in a single-blind, randomized, controlled trial if upregulation of emotion areas (NFE) yields superior efficacy compared to upregulation of a control region activated by visual scenes (NFS). Forty-three moderately to severely depressed medicated patients were randomly assigned to five sessions augmentation treatment of either NFE or NFS training. At primary outcome (week 12) no significant group mean HDRS difference was found (B = −0.415 [95% CI −4.847 to 4.016], p = 0.848) for the 32 completers (16 per group). However, across groups depressive symptoms decreased by 43%, and 38% of patients remitted. These improvements lasted until follow-up (week 18). Both groups upregulated target regions to a similar extent. Further, clinical improvement was correlated with an increase in self-efficacy scores. However, the interpretation of clinical improvements remains limited due to lack of a sham-control group. We thus surveyed effects reported for accepted augmentation therapies in depression. Data indicated that our findings exceed expected regression to the mean and placebo effects that have been reported for drug trials and other sham-controlled high-technology interventions. Taken together, we suggest that the experience of successful self-regulation during fMRI-NF training may be therapeutic. We conclude that if fMRI-NF is effective for depression, self-regulation training of higher visual areas may provide an effective alternative

BOLD fMRI complexity predicts changes in brain processes, interactions and patterns, in health and disease

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe human brain is the most complex information processing system that exists in nature. Its information processing functionality exists at multiple levels of interactions which can be influenced by electrical, chemical and physical components governed by thresholds and saturation phenomena [1]. When these thresholds are exceeded, saturation is reached, giving rise to nonlinear behaviour [22]. The human brain like most dynamic systems in nature typically exhibit chaotic and complex behaviours with nonlinear dynamic properties [3]